结合三维几何形状信息和二维纹理的3D目标匹配

doi:10.11772/j.issn.1001-9081.2014.05.1453

计算机应用 ›› 2014, Vol. 34 ›› Issue (5): 1453-1457.DOI: 10.11772/j.issn.1001-9081.2014.05.1453

结合三维几何形状信息和二维纹理的3D目标匹配

李水平,彭晓明

电子科技大学自动化工程学院，成都 611731

收稿日期:2013-10-30 修回日期:2014-01-13 发布日期:2014-05-30 出版日期:2014-05-01
通讯作者: 李水平
作者简介:李水平(1988-)，男，江西吉安人，硕士研究生，主要研究方向：三维建模、目标匹配、图像处理；彭晓明(1974-)，男，湖北武汉人，副教授，博士，主要研究方向：图像配准、图像识别、图像跟踪、三维图像处理。
基金资助:
中央高校基本科研业务费专项

3D object matching combined 3D geometrical shape and 2D texture feature

LI Shuiping,PENG Xiaoming

School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu Sichuan 611731, China

Received:2013-10-30 Revised:2014-01-13 Online:2014-05-30 Published:2014-05-01
Contact: LI Shuiping
Supported by:
the Fundamental Research Funds for the Central Universities

摘要/Abstract

摘要：

为了实现场景中三维目标与模型之间的匹配，提出了一种结合三维几何形状信息和二维纹理的三维目标匹配方法。首先提取场景中深度图像的尺度不变特征变换(SIFT)特征，用SIFT算法与三维模型重建时所用到的一系列2.5维深度图像进行一一匹配，找到与场景中目标姿态最为相似的深度图像，提取此深度图像的三维几何形状特征与模型进行匹配，实现模型的初始化，即将模型重置到与场景目标相接近的姿态。最后用融合二维纹理信息的迭代就近点（ICP）算法实现场景中目标与模型之间的匹配，从而得到场景中三维目标的准确姿态。实验结果验证了方法的可行性与精确性。

Abstract:

To solve the matching problem between the model and 3D object in the scenes, this paper presented a 3D object matching method combined 3D shape and 2D texture feature. Scale-Invariant Feature Transform (SIFT) feature was extracted from the range image in the scene, and then the range image matched with a series of 2.5 dimensional range images which were used for the 3D model reconstruction one by one based on SIFT algorithm, so that it could find out the most similar local range image to the object in the scene.The matching between this local range image and the object was completed through 3D shape feature. It is to initialize the model, in other words, it is to reset the model close to the object in the scene. At last, a Iterative Closest Point (ICP) algorithm combined with color was used to implement the matching between the object in the sences and the model which was reset before. In this way the pose of the object in the scene can be calculated accurately. The experimental results verify the feasibility and accuracy of the proposed method.

中图分类号:

TP391.41

李水平彭晓明. 结合三维几何形状信息和二维纹理的3D目标匹配[J]. 计算机应用, 2014, 34(5): 1453-1457.

LI Shuiping PENG Xiaoming. 3D object matching combined 3D geometrical shape and 2D texture feature[J]. Journal of Computer Applications, 2014, 34(5): 1453-1457.

参考文献

［1］WANG Y,EAM K. 2D affine-invariant contour matching using B-spline model ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007,29(10):1853-1858.
［2］THOURN K, KITJAIDURE Y. Robust multi-TAR of barycenter contour for multiple views shape matching and retrieval ［C］// Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics. Piscataway: IEEE Press, 2009:1894-1898.
［3］LIU T, SHI Z, YU H. Template-matching method based on image texture ［J］. Infrared and Laser Engineering, 2005,34(3):319-323.(刘铁军,史泽林,于海斌.一种基于图像纹理的模板匹配算法［J］.红外与激光工程,2005,34(3):319-323.)
［4］ALLAIRE S, KIM J J, BREEN S L, et al. Full orientation invariance and improved feature selectivity of 3D SIFT with application to medical image analysis ［C］// Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Piscataway: IEEE Press, 2008:1-8.
［5］JESSEN J B, PILZ F, KRAFT D, et al. Accumulation of different visual feature descriptors in a coherent framework ［C］// SCIA '11: Proceedings of the 17th Scandinavian Conference on Image Analysis. Berlin: Springer-Verlag, 2011: 79-90.
［6］WU C, CLIPP B, LI X, et al. 3D model matching with Viewpoint-Invariant Patches (VIP) ［C］// Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2008:1-8.
［7］RUSINKIEWICZ S, LEVOY M. Efficient variants of the ICP algorithm ［C］// Proceedings of the 2001 3rd International Conference on 3-D Digital Imaging and Modeling. Piscataway: IEEE Press, 2001:145-152.
［8］LOWE D G. Distinctive image features from scale invariant keypoints ［J］. International Journal of Computer Vision, 2004,60(2):91-110.
［9］MIAN A, BENNAMOUN M, OWENS R. On the repeatability and quality of keypoints for local feature-based 3D object retrieval from cluttered scenes ［J］. International Journal of Computer Vision, 2010,89(2/3):348-361.
［10］ANGEL E. Interactive computer graphics: a top-down approach using OpenGL ［M］. 5th ed. Boston: Addison-Wesley, 2009:425-430.
［11］GONZALEZ R C, WOODS R E. Digital image processing ［M］. 3rd ed. Upper Saddle River: Prentice Hall, 2008:138-140.
［12］SHI J, TOMASI C. Good features to track ［C］// Proceedings of the 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 1994:593-600.
［13］BESL P J, McKAY N D. A method for registration of 3-D shapes ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992,14(2):239-256.
［14］JOHNSON A E, KANG S B. Registration and integration of textured 3-D data ［C］// NRC '97: Proceedings of the International Conference on Recent Advances in 3-D Digital Imaging and Modeling. Washington, DC: IEEE Computer Society, 1997:234-241.
［15］DOUADI L, ALDON M J, CROSNIER A. Pair-wise registration of 3D/color data sets with ICP ［C］// Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway: IEEE Press, 2006: 663-668.

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结合三维几何形状信息和二维纹理的3D目标匹配

3D object matching combined 3D geometrical shape and 2D texture feature

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